Using these toolkits anyone will be able to use AI or machine learning platforms – which are as sophisticated as anything currently on the market, offering the potential to unleash vast innovation.

Over the past few months Google, Microsoft and Facebook have taken major decisions to make their artificial intelligence API’s openly available to all. IBM have opened their Watson API on a ‘freemium’ basis and Elon Musk launched the OpenAI project with a star-studded list of backers; Palantir CEO Peter Thiel, LinkedIn founder Reid Hoffman and Y Combinator president Sam Altman.

These hugely powerful tools, which are used, developed and backed by the world’s most advanced technology companies, are now available to anyone with the skills to use them.

Whilst these announcements haven’t quite drawn the media attention of an iPhone launch, their significance and reach may be far greater.

The point is this: using these toolkits, individually or combined, anyone can integrate transformational AI or machine learning platforms – which are as sophisticated as anything currently on the market – to their business at on a pay as you go or free basis.

So what does that mean? It means that any task currently performed by a costly black box AI platform such as identifying where to drill for oil, predicting disease outbreaks, optimising scientific experiments to develop new products or predictive maintenance can now be done in-house for the price of a data scientist’s salary or consultancy fee, using these open platforms and without losing any control or oversight of your data.

This is very important for a lot of the world’s businesses and they need to take it seriously. If its true potential is realised it will unleash a new generation of innovative startups that apply the latest AI techniques to disrupt the establishment.

Open AI and big business: an opportunity and a threat

Most big companies have a data strategy to help them make better decisions faster. In many cases these large, data heavy organisations rely on these enigmatic black boxes to turn their data into insights. These come with a high price tag and often involve allowing valuable data outside of their company walls and potentially out of their control.

Now, with the right people and expertise they can build their own tools to their specification whilst maintaining complete control of their valuable data. It could save them a lot of money and opens many new opportunities to use data in a more bespoke and business focused manner. And because they retain control, it allows them to better understand not just the hidden patterns in their data, but the reasons for those patterns and how to better convert that data into knowledge and actionable insight.

But this also presents a threat to the established order of things.

The high costs associated with black box platforms has meant only larger enterprises could afford them. But now small companies have access to the AI and machine learning previously only available to the big players.

A biotech startup can use state-of-the-art AI to derive the same level of data insights into its drug development processes as a traditional big pharma. Platforms developed on a computer science graduate’s laptops could turn data streams from connected cars into perfectly targeted insurance offerings, disrupting the incumbents and reshaping the marketplace.

Cloud platforms already enable small businesses to store their data on a scalable basis. Spotify was able to scale quickly, and change the entire music industry, because it was initially built upon the Amazon Web Services cloud. Add to that the incredible power to derive insights from your hosted data and we begin to see where the next business revolution might come from.

Skilful people, not infrastructure, are the future of the tech industry

However, this will not change the fact that data science is complicated. The barriers arising from the cost of scalable infrastructure may more or less disappear, but people who understand business and how to build analytics that derive value from data will become even more sought after.

Those able to combine advanced data science with a deep understanding of business challenges and domain expertise in areas such as life sciences, energy or industrial engineering will be the people who reshape companies in the next decade. They are the translators and their role is vital.

In short, the value of skill and expertise will overtake the value of technology. Received wisdom says modernisation runs from agriculture through manufacturing to services. This is another significant move of the modern world economy away from building physical products and towards harnessing intelligence to do things better.

New ways of working take a while to gain momentum and acceptance. Building AI platforms and getting people to use them is more complex than moving data to the cloud or buying a company iPad and its full impact will take a while to be felt beyond the early adopters.

But make no mistake, this has the potential to be hugely disruptive to the business world as we know it. Those hoping to remain, or become, market leaders in ten years’ time, should consider these changes very seriously.

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